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https://github.com/run-llama/llamaindexts
LlamaIndex is a data framework for your LLM applications
https://github.com/run-llama/llamaindexts
agent anthr chatbot claude claude-ai create-llama embedding firewo groq-ai javascript llama llama-index llama2 llama3 llm mistr nodejs openai react typescript
Last synced: 14 days ago
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LlamaIndex is a data framework for your LLM applications
- Host: GitHub
- URL: https://github.com/run-llama/llamaindexts
- Owner: run-llama
- License: mit
- Created: 2023-06-13T06:41:20.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-22T18:12:46.000Z (6 months ago)
- Last Synced: 2024-05-23T01:46:46.251Z (6 months ago)
- Topics: agent, anthr, chatbot, claude, claude-ai, create-llama, embedding, firewo, groq-ai, javascript, llama, llama-index, llama2, llama3, llm, mistr, nodejs, openai, react, typescript
- Language: TypeScript
- Homepage: https://ts.llamaindex.ai
- Size: 61.6 MB
- Stars: 1,423
- Watchers: 17
- Forks: 280
- Open Issues: 86
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-ChatGPT-repositories - LlamaIndexTS - Data framework for your LLM applications. Focus on server side solution (Chatbots)
README
# LlamaIndex.TS
[](https://www.npmjs.com/package/llamaindex)
[](https://www.npmjs.com/package/llamaindex)
[](https://www.npmjs.com/package/llamaindex)
[](https://discord.com/invite/eN6D2HQ4aX)
LlamaIndex is a data framework for your LLM application.
Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in JS runtime environments with TypeScript support.
Documentation: https://ts.llamaindex.ai/
Try examples online:
[](https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples)
## What is LlamaIndex.TS?
LlamaIndex.TS aims to be a lightweight, easy to use set of libraries to help you integrate large language models into your applications with your own data.
## Compatibility
### Multiple JS Environment Support
LlamaIndex.TS supports multiple JS environments, including:
- Node.js (18, 20, 22) ✅
- Deno ✅
- Bun ✅
- Nitro ✅
- Vercel Edge Runtime ✅ (with some limitations)
- Cloudflare Workers ✅ (with some limitations)
For now, browser support is limited due to the lack of support for [AsyncLocalStorage-like APIs](https://github.com/tc39/proposal-async-context)
### Supported LLMs:
- OpenAI LLms
- Anthropic LLms
- Groq LLMs
- Llama2, Llama3, Llama3.1 LLMs
- MistralAI LLMs
- Fireworks LLMs
- DeepSeek LLMs
- ReplicateAI LLMs
- TogetherAI LLMs
- HuggingFace LLms
- DeepInfra LLMs
- Gemini LLMs
## Getting started
```shell
npm install llamaindex
pnpm install llamaindex
yarn add llamaindex
```
### Setup TypeScript
```json5
{
compilerOptions: {
// ⬇️ add this line to your tsconfig.json
moduleResolution: "bundler", // or "node16"
},
}
```
Why?
We are shipping both ESM and CJS module, and compatible with Vercel Edge, Cloudflare Workers, and other serverless platforms.
So we are using [conditional exports](https://nodejs.org/api/packages.html#conditional-exports) to support all environments.
This is a kind of modern way of shipping packages, but might cause TypeScript type check to fail because of legacy module resolution.
Imaging you put output file into `/dist/openai.js` but you are importing `llamaindex/openai` in your code, and set `package.json` like this:
```json
{
"exports": {
"./openai": "./dist/openai.js"
}
}
```
In old module resolution, TypeScript will not be able to find the module because it is not follow the file structure, even you run `node index.js` successfully. (on Node.js >=16)
See more about [moduleResolution](https://www.typescriptlang.org/docs/handbook/modules/theory.html#module-resolution) or
[TypeScript 5.0 blog](https://devblogs.microsoft.com/typescript/announcing-typescript-5-0/#--moduleresolution-bundler7).
### Node.js
```ts
import fs from "node:fs/promises";
import { Document, VectorStoreIndex } from "llamaindex";
async function main() {
// Load essay from abramov.txt in Node
const essay = await fs.readFile(
"node_modules/llamaindex/examples/abramov.txt",
"utf-8",
);
// Create Document object with essay
const document = new Document({ text: essay });
// Split text and create embeddings. Store them in a VectorStoreIndex
const index = await VectorStoreIndex.fromDocuments([document]);
// Query the index
const queryEngine = index.asQueryEngine();
const response = await queryEngine.query({
query: "What did the author do in college?",
});
// Output response
console.log(response.toString());
}
main();
```
```bash
# `pnpm install tsx` before running the script
node --import tsx ./main.ts
```
### Next.js
You will need to add a llamaindex plugin to your Next.js project.
```js
// next.config.js
const withLlamaIndex = require("llamaindex/next");
module.exports = withLlamaIndex({
// your next.js config
});
```
### React Server Actions
You can combine `ai` with `llamaindex` in Next.js, Waku or Redwood.js with RSC (React Server Components).
```tsx
"use client";
import { chatWithAgent } from "@/actions";
import type { JSX } from "react";
import { useActionState } from "react";
export default function Home() {
const [ui, action] = useActionState(async () => {
return chatWithAgent("hello!", []);
}, null);
return (
{ui}
Chat
);
}
```
```tsx
// src/actions/index.ts
"use server";
import { createStreamableUI } from "ai/rsc";
import { OpenAIAgent } from "llamaindex";
import type { ChatMessage } from "llamaindex/llm/types";
export async function chatWithAgent(
question: string,
prevMessages: ChatMessage[] = [],
) {
const agent = new OpenAIAgent({
tools: [
// ... adding your tools here
],
});
const responseStream = await agent.chat(
{
message: question,
chatHistory: prevMessages,
},
true,
);
const uiStream = createStreamableUI(
loading...);
responseStream
.pipeTo(
new WritableStream({
start: () => {
uiStream.update("response:");
},
write: async (message) => {
uiStream.append(message.response.delta);
},
}),
)
.catch(console.error);
return uiStream.value;
}
```
### Cloudflare Workers
> [!TIP]
> Some modules are not supported in Cloudflare Workers which require Node.js APIs.
```ts
// add `OPENAI_API_KEY` to the `.dev.vars` file
interface Env {
OPENAI_API_KEY: string;
}
export default {
async fetch(
request: Request,
env: Env,
ctx: ExecutionContext,
): Promise {
const { OpenAIAgent, OpenAI } = await import("@llamaindex/openai");
const text = await request.text();
const agent = new OpenAIAgent({
llm: new OpenAI({
apiKey: env.OPENAI_API_KEY,
}),
tools: [],
});
const responseStream = await agent.chat({
stream: true,
message: text,
});
const textEncoder = new TextEncoder();
const response = responseStream.pipeThrough(
new TransformStream({
transform: (chunk, controller) => {
controller.enqueue(textEncoder.encode(chunk.delta));
},
}),
);
return new Response(response);
},
};
```
### Vite
We have some wasm dependencies for better performance. You can use `vite-plugin-wasm` to load them.
```ts
import wasm from "vite-plugin-wasm";
export default {
plugins: [wasm()],
ssr: {
external: ["tiktoken"],
},
};
```
### Tips when using in non-Node.js environments
When you are importing `llamaindex` in a non-Node.js environment(such as Vercel Edge, Cloudflare Workers, etc.)
Some classes are not exported from top-level entry file.
The reason is that some classes are only compatible with Node.js runtime,(e.g. `PDFReader`) which uses Node.js specific APIs(like `fs`, `child_process`, `crypto`).
If you need any of those classes, you have to import them instead directly though their file path in the package.
Here's an example for importing the `PineconeVectorStore` class:
```typescript
import { PineconeVectorStore } from "llamaindex/storage/vectorStore/PineconeVectorStore";
```
As the `PDFReader` is not working with the Edge runtime, here's how to use the `SimpleDirectoryReader` with the `LlamaParseReader` to load PDFs:
```typescript
import { SimpleDirectoryReader } from "llamaindex/readers/SimpleDirectoryReader";
import { LlamaParseReader } from "llamaindex/readers/LlamaParseReader";
export const DATA_DIR = "./data";
export async function getDocuments() {
const reader = new SimpleDirectoryReader();
// Load PDFs using LlamaParseReader
return await reader.loadData({
directoryPath: DATA_DIR,
fileExtToReader: {
pdf: new LlamaParseReader({ resultType: "markdown" }),
},
});
}
```
> _Note_: Reader classes have to be added explictly to the `fileExtToReader` map in the Edge version of the `SimpleDirectoryReader`.
You'll find a complete example with LlamaIndexTS here: https://github.com/run-llama/create_llama_projects/tree/main/nextjs-edge-llamaparse
## Playground
Check out our NextJS playground at https://llama-playground.vercel.app/. The source is available at https://github.com/run-llama/ts-playground
## Core concepts for getting started:
- [Document](/packages/llamaindex/src/Node.ts): A document represents a text file, PDF file or other contiguous piece of data.
- [Node](/packages/llamaindex/src/Node.ts): The basic data building block. Most commonly, these are parts of the document split into manageable pieces that are small enough to be fed into an embedding model and LLM.
- [Embedding](/packages/llamaindex/src/embeddings/OpenAIEmbedding.ts): Embeddings are sets of floating point numbers which represent the data in a Node. By comparing the similarity of embeddings, we can derive an understanding of the similarity of two pieces of data. One use case is to compare the embedding of a question with the embeddings of our Nodes to see which Nodes may contain the data needed to answer that question. Because the default service context is OpenAI, the default embedding is `OpenAIEmbedding`. If using different models, say through Ollama, use this [Embedding](/packages/llamaindex/src/embeddings/OllamaEmbedding.ts) (see all [here](/packages/llamaindex/src/embeddings)).
- [Indices](/packages/llamaindex/src/indices/): Indices store the Nodes and the embeddings of those nodes. QueryEngines retrieve Nodes from these Indices using embedding similarity.
- [QueryEngine](/packages/llamaindex/src/engines/query/RetrieverQueryEngine.ts): Query engines are what generate the query you put in and give you back the result. Query engines generally combine a pre-built prompt with selected Nodes from your Index to give the LLM the context it needs to answer your query. To build a query engine from your Index (recommended), use the [`asQueryEngine`](/packages/llamaindex/src/indices/BaseIndex.ts) method on your Index. See all query engines [here](/packages/llamaindex/src/engines/query).
- [ChatEngine](/packages/llamaindex/src/engines/chat/SimpleChatEngine.ts): A ChatEngine helps you build a chatbot that will interact with your Indices. See all chat engines [here](/packages/llamaindex/src/engines/chat).
- [SimplePrompt](/packages/llamaindex/src/Prompt.ts): A simple standardized function call definition that takes in inputs and formats them in a template literal. SimplePrompts can be specialized using currying and combined using other SimplePrompt functions.
## Contributing:
Please see our [contributing guide](CONTRIBUTING.md) for more information.
You are highly encouraged to contribute to LlamaIndex.TS!
## Community
Please join our Discord! https://discord.com/invite/eN6D2HQ4aX